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2 times 3 can sometimes equal 7 with Android's Neural Network API

alexanderganderson.github.io

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Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#21
post #16

Earlier quoted context omitted.

Do you want to die when your self driving car crashes? Debug issues when your app des at 12am? Same concept.

I don't want to die when I crash my own car, and I already debug my own apps at 12am. If your argument is that things need to be perfect than my god you must never leave your home! I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. That considered, I had a p…

> I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things.

Humans are much, much, much more capable than the absolute state-of-the-art robots when it comes to doing things in an uncontrolled environment.

https://www.youtube.com/watch?v=g0TaYhjpOfo

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#25
post #9
post #7

Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.

Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers. I think it'll make neat autopilot functionality for developers but not replace the need to have someone look over and understand the code.

Its not that simple.

People are understanding when car crashes happen in busy roads amongst other cars.

They are not understanding if a self-driving car swerves into the sidewalk and kills a group of children.

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#26
post #7

Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.

For code, I could see it being super useful for a beefed up auto-complete. There are many times I find myself searching for things like "how do I do X in Y language" to copy a snippet that I'm sure has been written 10000x times before. I can review the code and verify its correctness by writing tests.

[deleted]

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#27

Famous Pentium F-DIV 20 years later, the sequel?

It's a neural network. It gives approximate results. Here's a newbie question that asks basically the same question, with some interesting answers.

> codesternews: Any deeplearning expert here. Why Neural network can't compute a linear function Celsius to Fahrenheit 100% accurately. Is it data or is it something can be optimised.

    print(model.predict([100.0]))
    // it results 211.874 which is not 100% accurate (100×1.8+32=212)
https://news.ycombinator.com/item?id=19708787

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#28
post #9
post #7

Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.

Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers. I think it'll make neat autopilot functionality for developers but not replace the need to have someone look over and understand the code.

I disagree that that is enough to be useful. To give a deliberately extreme example: if it produces code which has half the number of bugs as a human, but it only outputs Malbolge source code, nobody else will be able to fix those bugs which remain.

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#29
post #16

Earlier quoted context omitted.

I don't want to die when I crash my own car, and I already debug my own apps at 12am. If your argument is that things need to be perfect than my god you must never leave your home! I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. That considered, I had a p…

> I'd trust a machine to drive more accurately than most people I see on the highway. Humans aren't special, in fact more often than not we're sloppy, subject to fatigue, and a whole bunch of other negative things. Humans are much, much, much more capable than the absolute state-of-the-art robots when it comes to doing things in an uncontrolled environment. https://www.youtube.com/watch?v=g0TaYhjpOfo

One of the advantages of an autonomous driver is that its superhuman reflexes, never driving while tired, never getting road rage, etc., will make it less likely to get into an uncontrolled environment.

Would you prefer your pilots to fly your plane with no AI assistance?

Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API

#30
post #9
post #7

Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.

Code generation only needs to generate code with n bugs where n is less than the number of bugs a human developer generates for it to have usefulness, and maybe some other factor of severity where they are generally less severe than human developers. I think it'll make neat autopilot functionality for developers but not replace the need to have someone look over and understand the code.

This is a very simplistic of what code is and the role it plays in a system.

There are many implementations that can fulfill a set of requirements. Not all of them are created equal. The ways in which they behave as the system changes can be wildly different. Well-written code will be able to handle those changes gracefully. Poorly-written code may end up proving brittle and bug-prone. Generated code will be completely unpredictable.

Imagine you're trying to build a street network for a city. Some designs are much more predictable than others. If you've played Factorio, the distinction between a spaghetti base and one that has some design is abundant. Even if they currently fulfill the same requirements now, the ability to improve upon and reason about how it will behave after changes is vastly different.

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